

import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCAmelCase     						=      16
_UpperCAmelCase     						=      32


def       __UpperCamelCase     (lowerCAmelCase       :	Tuple,     lowerCAmelCase       :	Any,     lowerCAmelCase       :	List[str],     lowerCAmelCase       :	Tuple,     lowerCAmelCase       :	Optional[int] = 16  )  ->	int:
      A 			=					AutoTokenizer.from_pretrained('bert-base-cased'  )
      A 			=					DatasetDict(
          {
              'train': dataset['train'].select(lowerCAmelCase  ),
              'validation': dataset['train'].select(lowerCAmelCase  ),
              'test': dataset['validation'],
          }  )

      def tokenize_function(lowerCAmelCase       :	List[str]  ):
            # max_length=None => use the model max length (it's actually the default)
            A 			=					tokenizer(examples['sentence1'],     examples['sentence2'],     truncation=lowerCAmelCase,     max_length=lowerCAmelCase  )
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            A 			=					datasets.map(
                lowerCAmelCase,     batched=lowerCAmelCase,     remove_columns=['idx', 'sentence1', 'sentence2'],     )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      A 			=					tokenized_datasets.rename_column('label',     'labels'  )

      def collate_fn(lowerCAmelCase       :	Dict  ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            A 			=					128 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  A 			=					16
            elif accelerator.mixed_precision != "no":
                  A 			=					8
            else:
                  A 			=					None

            return tokenizer.pad(
                lowerCAmelCase,     padding='longest',     max_length=lowerCAmelCase,     pad_to_multiple_of=lowerCAmelCase,     return_tensors='pt',     )

      # Instantiate dataloaders.
      A 			=					DataLoader(
          tokenized_datasets['train'],     shuffle=lowerCAmelCase,     collate_fn=lowerCAmelCase,     batch_size=lowerCAmelCase  )
      A 			=					DataLoader(
          tokenized_datasets['validation'],     shuffle=lowerCAmelCase,     collate_fn=lowerCAmelCase,     batch_size=lowerCAmelCase  )

      A 			=					DataLoader(
          tokenized_datasets['test'],     shuffle=lowerCAmelCase,     collate_fn=lowerCAmelCase,     batch_size=lowerCAmelCase  )

      return train_dataloader, eval_dataloader, test_dataloader


def       __UpperCamelCase     (lowerCAmelCase       :	int,     lowerCAmelCase       :	str  )  ->	Optional[Any]:
      # New Code #
      A 			=					[]
      # Download the dataset
      A 			=					load_dataset('glue',     'mrpc'  )
      # Create our splits
      A 			=					StratifiedKFold(n_splits=int(args.num_folds  )  )
      # Initialize accelerator
      A 			=					Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision  )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      A 			=					config["""lr"""]
      A 			=					int(config['num_epochs']  )
      A 			=					int(config['seed']  )
      A 			=					int(config['batch_size']  )

      A 			=					evaluate.load('glue',     'mrpc'  )

      # If the batch size is too big we use gradient accumulation
      A 			=					1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            A 			=					batch_size // MAX_GPU_BATCH_SIZE
            A 			=					MAX_GPU_BATCH_SIZE

      set_seed(lowerCAmelCase  )

      # New Code #
      # Create our folds:
      A 			=					kfold.split(np.zeros(datasets['train'].num_rows  ),     datasets['train']['label']  )
      A 			=					[]
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase  ):
            A 			=					get_fold_dataloaders(
                lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     )
            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            A 			=					AutoModelForSequenceClassification.from_pretrained('bert-base-cased',     return_dict=lowerCAmelCase  )

            # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
            # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
            # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
            A 			=					model.to(accelerator.device  )

            # Instantiate optimizer
            A 			=					AdamW(params=model.parameters(),     lr=lowerCAmelCase  )

            # Instantiate scheduler
            A 			=					get_linear_schedule_with_warmup(
                optimizer=lowerCAmelCase,     num_warmup_steps=100,     num_training_steps=(len(lowerCAmelCase  ) * num_epochs) // gradient_accumulation_steps,     )

            # Prepare everything
            # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
            # prepare method.
            A 			=					accelerator.prepare(
                lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase  )

            # Now we train the model
            for epoch in range(lowerCAmelCase  ):
                  model.train()
                  for step, batch in enumerate(lowerCAmelCase  ):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device  )
                        A 			=					model(**lowerCAmelCase  )
                        A 			=					outputs.loss
                        A 			=					loss / gradient_accumulation_steps
                        accelerator.backward(lowerCAmelCase  )
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                  model.eval()
                  for step, batch in enumerate(lowerCAmelCase  ):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device  )
                        with torch.no_grad():
                              A 			=					model(**lowerCAmelCase  )
                        A 			=					outputs.logits.argmax(dim=-1  )
                        A 			=					accelerator.gather_for_metrics((predictions, batch['labels'])  )
                        metric.add_batch(
                            predictions=lowerCAmelCase,     references=lowerCAmelCase,     )

                  A 			=					metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(f'''epoch {epoch}:''',     lowerCAmelCase  )

            # New Code #
            # We also run predictions on the test set at the very end
            A 			=					[]
            for step, batch in enumerate(lowerCAmelCase  ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device  )
                  with torch.no_grad():
                        A 			=					model(**lowerCAmelCase  )
                  A 			=					outputs.logits
                  A 			=					accelerator.gather_for_metrics((predictions, batch['labels'])  )
                  fold_predictions.append(predictions.cpu()  )
                  if i == 0:
                        # We need all of the test predictions
                        test_references.append(references.cpu()  )
        # Use accelerator.print to print only on the main process.
            test_predictions.append(torch.cat(lowerCAmelCase,     dim=0  )  )
            # We now need to release all our memory and get rid of the current model, optimizer, etc
            accelerator.free_memory()
      # New Code #
      # Finally we check the accuracy of our folded results:
      A 			=					torch.cat(lowerCAmelCase,     dim=0  )
      A 			=					torch.stack(lowerCAmelCase,     dim=0  ).sum(dim=0  ).div(int(args.num_folds  )  ).argmax(dim=-1  )
      A 			=					metric.compute(predictions=lowerCAmelCase,     references=lowerCAmelCase  )
      accelerator.print('Average test metrics from all folds:',     lowerCAmelCase  )


def       __UpperCamelCase     ()  ->	Optional[int]:
      A 			=					argparse.ArgumentParser(description='Simple example of training script.'  )
      parser.add_argument(
          '--mixed_precision',     type=lowerCAmelCase,     default=lowerCAmelCase,     choices=['no', 'fp16', 'bf16', 'fp8'],     help='Whether to use mixed precision. Choose'
          'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
          'and an Nvidia Ampere GPU.',     )
      parser.add_argument('--cpu',     action='store_true',     help='If passed, will train on the CPU.'  )
      # New Code #
      parser.add_argument('--num_folds',     type=lowerCAmelCase,     default=3,     help='The number of splits to perform across the dataset'  )
      A 			=					parser.parse_args()
      A 			=					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(lowerCAmelCase,     lowerCAmelCase  )


if __name__ == "__main__":
  main()





lowerCamelCase_    :			Tuple    =	{
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
lowerCamelCase_    :			Union[str, Any]    =	{
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						float:
				UpperCamelCase_:				Any						=    from_type.lower().strip("""s"""    )
				UpperCamelCase_:				int						=    to_type.lower().strip("""s"""    )

				UpperCamelCase_:				Any						=    UNIT_SYMBOL.get(lowerCamelCase  ,     lowerCamelCase    )
				UpperCamelCase_:				str						=    UNIT_SYMBOL.get(lowerCamelCase  ,     lowerCamelCase    )

				if from_sanitized not in METRIC_CONVERSION:
								UpperCamelCase_:				Optional[int]						=    (
								    F'''Invalid \'from_type\' value: {from_type!r}.\n'''
								    F'''Conversion abbreviations are: {", ".join(lowerCamelCase    )}'''
								)
								raise ValueError(lowerCamelCase    )
				if to_sanitized not in METRIC_CONVERSION:
								UpperCamelCase_:				Dict						=    (
								    F'''Invalid \'to_type\' value: {to_type!r}.\n'''
								    F'''Conversion abbreviations are: {", ".join(lowerCamelCase    )}'''
								)
								raise ValueError(lowerCamelCase    )
				UpperCamelCase_:				Union[str, Any]						=    METRIC_CONVERSION[from_sanitized]
				UpperCamelCase_:				str						=    METRIC_CONVERSION[to_sanitized]
				UpperCamelCase_:				Tuple						=    1

				if from_exponent > to_exponent:
								UpperCamelCase_:				Union[str, Any]						=    from_exponent - to_exponent
				else:
								UpperCamelCase_:				Dict						=    -(to_exponent - from_exponent)

				return value * pow(10  ,     lowerCamelCase    )


if __name__ == "__main__":
							from doctest import testmod

							testmod()






